The key to profitability in the semiconductor industry is yield management. Virtually any integrated circuit manufacturer would be profitable if they could have a manufacturing process that had a 100% yield, and certainly would be unprofitable if the yield were below 20%. To obtain a high yield rate is an on-going task of manufacturers that requires continuous monitoring and identifying production problems that are adversely impacting yield.
Typically, a semiconductor wafer may have a very large number of defects (e.g., more than 1000) which may have resulted from a great number of causations. Because, in general, the number of defects is very high, it is extremely time-consuming and expensive for an operator to examine each and every defect, and for that reason such a thorough examination of a wafer is infrequently performed.
In general there are two broad categories that cause yield reduction in the manufacturing process: handling problems; and process problems. Grouped defects typically have a common causation, some of the well known causations are chemical smears, grain, haze, microscoring, wand marks, brush marks, rail marks, impact marks, hot spots, shower patterns, exposure shutter patterns, doughnut patterns, poorly or unevenly developed layers on the die, over etching, under etching, thermal variations, etc.
Defects, no matter what the cause, have their own signatures which can be used to identify the causal mechanism of that particular defect, or defect group or pattern. Thus, it has also been determined that defects having the same causal relationship are likely to be statistically similar in their properties, such as location, proximity on the wafer, size and shape, as well as the shape of the defect pattern or cluster of defects resulting from the same causal mechanism.
Typically, an assessment of the state of a manufacturing process involves:
1. Selection of the set of manufactured articles to inspect; PA1 2. Inspection of the selected article(s); PA1 3. Selection of defects to review--all defects detected, or a selected sample; PA1 4. Review of selected defects; PA1 5. Analysis of inspection and review results; and PA1 6. Determination of process control action to be taken. (This may include a decision of which articles to inspect next.) PA1 1. Defects to be reviewed are selected (This may be all of the defects detected during inspection, or a manually or automatically selected sample); PA1 2. The operator views the defect, or an image of the defect using a microscope, scanning electron microscope (SEM), etc.; and PA1 3. The operator assigns a defect type classification, typically a numerical code, to the defect based on preselected criteria. PA1 1. The operator manually searches for clusters in the set of defect data (i.e., on a map showing all defects on a particular wafer--a wafer map); PA1 2. The operator manually selects some defects from each cluster; and PA1 3. Based on the types of defects identified in the cluster, a type classification is assigned to the cluster, and to all defects in the cluster. PA1 1. The number of defects detected during inspection that do not significantly affect yield can overwhelm the review process. PA1 2. Reviewing all the defects detected is too expensive, both in the amount of time and in the amount of human labor required. This leads one to review a sample of the detected defects. Manual selection of samples is highly subjective (i.e., not random.) PA1 3. A large delay while manually reviewing defects in a manufacturing process is undesirable, having one or all of the following negative effects: PA1 4. Some causal mechanisms produce a single defect event that is wrongly detected as a group of defect events having a common causal mechanism by an inspection machine. SPC that uses this defect count as an input is adversely affected. PA1 5. Each defect cluster is assumed to be caused by a single causality. However, an accurate estimate of the number of defects in a cluster, and by implication the total number of clustered defects, is impractical to obtain manually. Without this correction to the defect count, SPC decisions will be based on an incorrect defect count. For example, in semiconductor processing, a radical increase in the number of detected defects on a small number of chips on a single wafer is not significant for SPC purposes, because such excursions do not significantly affect yield. PA1 6. The inspection tool makes numerous measurements of defect properties, the results of which are not generally available during manual defect review. Manual cluster classification accuracy is therefore less than optimal. PA1 7. The results of repeating defect detection may be skewed by the presence of clusters. PA1 8. The results of defect source analysis may be skewed by defect events that cover more than one inspected article. PA1 9. The review process does not take into account information previously acquired during inspection. Review typically continues until a specific number of defects, or the entire set of defects, have been reviewed.
In semiconductor manufacturing, integrated circuit inspection is typically performed between selected process steps. These inspections also are performed automatically with a machine designed specifically for this task with those machines typically performing either optical inspections or electrical tests. The meaning of inspection in this instance is the detection of observed differences between the expected product and the actual product either in appearance or electrical performance. The principle output of an inspection machine is typically the number of detected defects, a list of defect locations, and some measured properties of each defect.
Defects detected are often much larger than the detection system resolution used by the inspection system, but are usually reported in terms of the detection system resolution. A single defect event may cover an area many orders of magnitude larger than the inspection system resolution. As a result, a single defect event may be detected as multiple defects (e.g., a scratch may have several line segments that make up the total scratch). Thus, if the defect events are misidentified or miscounted there will be a distortion of the assessment of the state of the process that resulted in the defects, leading to incorrect process control decisions to correct the source of the defects.
After detection, defects are often reviewed manually with defect review being a process of assigning defect classification types to detected defects. Review strategies may vary, but in general that review proceeds as follows:
Since it is generally believed that a high degree of spatial correlation among a group of defects usually indicates a common defect causality, manual clustering is occasionally used during review:
Inspection and review data are typically analyzed in a number of ways. For example, correlation studies of final manufacturing yield versus each defect type are performed in order to prioritize the importance of defect types. Based significantly on this analysis, it may be determined that some defect types do not detract from yield. In other words, if one is using defect count as a Statistical Process Control (SPC) parameter, these defect types should not be included in the defect count.
Correlation studies of defect locations on a given wafer inspected at one step in the manufacturing process may be performed against the inspection results for the same wafer at a different process step, in order to determine the process step that is the source of particular defects or defect types. Identification of the processing step that is the source of a defect is a necessary condition for eliminating that source of the defects. Correlation studies of defects on one die may also be compared against other dies to identify repeating defects.
There are a number of problems with the prior art approach outlined above. Some of those problems are:
a. A smaller number of items will be inspected (because there is insufficient review capacity), giving a less accurate assessment of the state of the process, or PA2 b. A smaller percentage of defects detected will be reviewed, giving an assessment of the process state that may be skewed by bias in the manual sample selection, or PA2 c. The manufacturing process must be slowed down--which is generally unacceptable.
It would prove very beneficial if group defects could be placed into certain meaningful cluster approximations of those groups with each cluster having a related causality which would make defect data much more manageable. Then it would be even more beneficial if that more manageable cluster defect data could be processed automatically to identify corrective actions necessary to reduce the number of defect groups, and therefore total number of defects, that occur in later production runs. The present invention provide such a system.